Fuzzy clustering of gradient-echo functional MRI in the human visual cortex. Part II: Quantification
Corresponding Author
Ewald Moser PhD
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, Austria
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, AustriaSearch for more papers by this authorMarkus Diemling MSc
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, Austria
Search for more papers by this authorRichard Baumgartner MSc
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, Austria
Search for more papers by this authorCorresponding Author
Ewald Moser PhD
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, Austria
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, AustriaSearch for more papers by this authorMarkus Diemling MSc
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, Austria
Search for more papers by this authorRichard Baumgartner MSc
Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Waehringerstrasse 13, A-1090 Vienna, Austria
Search for more papers by this authorAbstract
Fuzzy cluster analysis (FCA) is a new exploratory method for analyzing fMRI data. Using simulated functional MRI (fMRI) data, the performance of FCA, as implemented in the software package Evident, was tested and a quantitative comparison with correlation analysis is presented. Furthermore, the fMRI model fit allows separation and quantification of flow and blood oxygen level dependent (BOLD) contributions in the human visual cortex. In gradient-recalled echo fMRI at 1.5 T (TR = 60 ms, TE = 42 ms, radiofrequency excitation flip angle [ϑ] = 10°–60°) total signal enhancement in the human visual cortex, ie, flow-enhanced BOLD plus inflow contributions, on average varies from 5% to 10% in or close to the visual cortex (average cerebral blood volume [CBV] = 4%) and from 10% to 20% in areas containing medium-sized vessels (ie, average CBV = 12% per voxel), respectively. Inflow enhancement, however, is restricted to intravascular space (= CBV) and increases with increasing radiofrequency (RF) flip angle, whereas BOLD contributions may be obtained from a region up to three times larger and, applying an unspoiled gradient-echo (GRE) sequence, also show a flip angle dependency with a minimum at approximately 30°. This result suggests that a localized hemodynamic response from the microvasculature at 1.5 T maybe extracted via fuzzy clustering. In summary, fuzzy clustering of fMRI data, as realized in the Evident software, is a robust and efficient method to (a) separate functional brain activation from noise or other sources resulting in time-dependent signal changes as proven by simulated fMRI data analysis and in vivo data from the visual cortex, and (b) allows separation of different levels of activation even if the temporal pattern is indistinguishable. Combining fuzzy cluster separation of brain activation with appropriate model calculations allows quantification of flow and (flow-enhanced) BOLD contributions in areas with different vascularization.
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